store = {}
store['args']={'name': 'emnist_multibald_bald_k10_629535', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 629535, 'acquisition_method': 'AcquisitionMethod.multibald', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 1, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=1', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.02303191489361702, 'nll': 3.874142925587106}, 'chosen_samples': [73295, 87703, 39163, 81347, 64813], 'chosen_samples_score': [0.02884942645804145, 0.05730234788218791, 0.0853034403990991, 0.11000806497490423, 0.13567958033954675], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06111702127659575, 'nll': 36.01483167283079}, 'chosen_samples': [7382, 80874, 99991, 86574, 81495], 'chosen_samples_score': [1.0485048957383711, 1.6283998917923086, 1.964944643038036, 2.140406911472448, 2.229808949758853], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.0948404255319149, 'nll': 35.0631856553098}, 'chosen_samples': [76491, 103429, 51268, 27415, 99960], 'chosen_samples_score': [1.5027982812802507, 2.136896330245313, 2.2613143104626934, 2.296962522797165, 2.2950745647922215], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.10851063829787234, 'nll': 29.11288466920244}, 'chosen_samples': [50860, 61880, 8481, 10879, 24865], 'chosen_samples_score': [1.6075743733416652, 2.171214287026514, 2.2674752250154677, 2.3014168374178396, 2.311714247067561], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.13398936170212766, 'nll': 29.463052416862325}, 'chosen_samples': [12627, 57656, 4477, 19403, 111737], 'chosen_samples_score': [1.5405274322522085, 2.217204624028133, 2.285506664055097, 2.30137658185265, 2.318531662703405], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.1524468085106383, 'nll': 31.867149710147938}, 'chosen_samples': [9566, 65846, 55241, 104269, 52141], 'chosen_samples_score': [1.60285026907289, 2.173790857194941, 2.289284632907422, 2.3061652049529875, 2.282413537340746], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.14973404255319148, 'nll': 26.96096189945302}, 'chosen_samples': [85765, 94908, 52554, 74919, 38410], 'chosen_samples_score': [1.4971855561789877, 2.123306994867634, 2.275673510693029, 2.3050748905140437, 2.3036809180239577], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.16484042553191489, 'nll': 25.113382691728308}, 'chosen_samples': [24412, 109885, 106701, 41215, 66922], 'chosen_samples_score': [1.6785345028910768, 2.234202030816064, 2.2969314463367, 2.306311563338623, 2.303988668009402], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1895744680851064, 'nll': 23.821716581304024}, 'chosen_samples': [7810, 13485, 108463, 55415, 95215], 'chosen_samples_score': [1.5786706278717184, 2.1621130504667745, 2.2758567110312615, 2.3018400090645637, 2.279221025640216], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.19484042553191488, 'nll': 21.18346886817445}, 'chosen_samples': [89247, 61026, 55889, 84492, 53077], 'chosen_samples_score': [1.6030052816192943, 2.217957847113328, 2.2930892921396158, 2.3204302026729473, 2.3127447782057193], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.21877659574468086, 'nll': 21.566140818494432}, 'chosen_samples': [67773, 102130, 16068, 21794, 44600], 'chosen_samples_score': [1.7996001724680506, 2.2492605974987194, 2.294606083218789, 2.2953758830238877, 2.283554666013891], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22531914893617022, 'nll': 19.37326399620543}, 'chosen_samples': [112072, 109266, 13694, 45490, 25847], 'chosen_samples_score': [1.7283560066213584, 2.24899584055767, 2.2984169283829963, 2.2994557188844156, 2.3071649827441565], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2276063829787234, 'nll': 18.34821662253522}, 'chosen_samples': [79224, 1323, 8225, 68318, 8229], 'chosen_samples_score': [1.7426283013382617, 2.1947711677319, 2.2897973548858332, 2.309853494284594, 2.3019331360680124], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23547872340425532, 'nll': 17.57701949748587}, 'chosen_samples': [54548, 25872, 73711, 84249, 109256], 'chosen_samples_score': [1.6479719590651662, 2.184215362496242, 2.2788263860475038, 2.3063028240933447, 2.291335792931994], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.23404255319148937, 'nll': 16.39640603329273}, 'chosen_samples': [49362, 21523, 67690, 49503, 53463], 'chosen_samples_score': [1.7198172381854915, 2.2368846759462535, 2.2954943655406987, 2.281154358984641, 2.3046444649580926], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.27372340425531916, 'nll': 14.925303101235247}, 'chosen_samples': [72859, 84713, 1006, 99698, 27156], 'chosen_samples_score': [1.6684217435112136, 2.2315981130675535, 2.294389789729932, 2.286079381239961, 2.3010816417483158], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2594148936170213, 'nll': 13.91351547241211}, 'chosen_samples': [69685, 23588, 86826, 95681, 102425], 'chosen_samples_score': [1.7245036189252845, 2.259126739819732, 2.2972072094972806, 2.2923074465588504, 2.3059433792181028], 'chosen_samples_orignal_score': None})
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